Various methods are known for predicting future travel times for a target vehicle traveling on a route based on probe data collected by probe vehicles for the same route. The route includes links or sequential road segments. The methods include linear or nonlinear regression, non-parametric regression, neural networks, and support vector machines (SVM). The methods can use locations, current times and date of the target vehicle and compare that data to similar probe data acquired by the probe vehicle. Having a good estimate of the current travel time can significantly improve the prediction of future travel times.
It is important to predict future travel times for each link on the route, instead of simply using the current travel time estimates for each link. This is because a particular route can take hours to travel, and the current travel time can be very inaccurate, e.g., one or two hours later, particularly as traffic conditions change.
On less traveled roads, new probe data from the probe vehicles can be very infrequent and sparse. If there is no recent probe data for a link, then it is difficult to predict the current travel time on the link. For a method that relies on an estimate of the current travel time to make a prediction of future travel times, this is a major problem.
U.S. Pat. No. 7,894,980 describes a method for estimating real-time travel times or traffic loads in a transportation network based on limited real-time data. That method estimates traffic flow, i.e., number of vehicles on a link per unit time, from real time data on one link and using that estimate to predict flow on a connected link based on knowledge of splitting percentages, which is the percentage of vehicles that leave the link instead of continuing along the link.
U.S. Pat. No. 7,375,649 describes a method for identifying a fastest possible travel route. A traveler data processor collects traffic speed data and associates the traffic speed data with road segments. The road segments collectively represent one or more possible travel routes from a start point to an end point. A forecast engine determines a predicted travel time for each of the road segments based on the traffic speed data for each of the road segments. A routing engine determines the fastest possible travel route from the start point to the end point.
U.S. Publication 20120290204 describes a system for predicting a travel time for a traffic route including one or more road segments. A predicted travel time for each of those segments is based on traffic speed data for each road segments. A total travel time is then calculated for the route.
U.S. Publication 20120136561 describes a method predicting traffic conditions at future times by using probabilistic techniques for road segments in real-time based on changing current conditions for a network of roads. One or more predictive Bayesian models and corresponding decision trees are created based on based on historical traffic conditions.
U.S. Publication 20100063715 describes a method for predicting traffic on a transportation network where real time data points are missing. The missing data are estimated using a calibration model of historical data that can be periodically updated, from select links constituting a relationship vector. The missing data can be estimated off-line for part of the network.
Most prior art method use historical data for the prediction. This is a problem because the historical data may not reflect current traffic conditions.